Williams, NJ and Daly, Ian and Nasuto, SJ (2018) Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Frontiers in Computational Neuroscience, 12. 76-. DOI https://doi.org/10.3389/fncom.2018.00076
Williams, NJ and Daly, Ian and Nasuto, SJ (2018) Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Frontiers in Computational Neuroscience, 12. 76-. DOI https://doi.org/10.3389/fncom.2018.00076
Williams, NJ and Daly, Ian and Nasuto, SJ (2018) Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Frontiers in Computational Neuroscience, 12. 76-. DOI https://doi.org/10.3389/fncom.2018.00076
Abstract
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.
Item Type: | Article |
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Uncontrolled Keywords: | EEG/MEG dynamic connectivity, EEG/MEG time-varying networks, sparse-MVAR modeling, markov modeling, granger causality, effective connectivity |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 13 Nov 2018 13:30 |
Last Modified: | 30 Oct 2024 16:45 |
URI: | http://repository.essex.ac.uk/id/eprint/23468 |
Available files
Filename: fncom-12-00076.pdf
Licence: Creative Commons: Attribution 3.0